Epilepsy, a neurological disorder characterized by the occurrence of seizures (specifically episodic impairment or loss of consciousness, abnormal motor phenomena, psychic or sensory disturbances, or the perturbation of the autonomic nervous system), is debilitating to a great number of people. It is believed that as many as two to four million Americans may suffer from various forms of epilepsy. Research has found that its prevalence may be even greater worldwide, particularly in less economically developed nations, suggesting that the worldwide figure for epilepsy sufferers may be in excess of one hundred million.
Because epilepsy is characterized by seizures, its sufferers are frequently limited in the kinds of activities they may participate in. Epilepsy can prevent people from driving, working, or otherwise participating in much of what society has to offer. Some epilepsy sufferers have serious seizures so frequently that they are effectively incapacitated.
Furthermore, epilepsy is often progressive and can be associated with degenerative disorders and conditions. Over time, epileptic seizures often become more frequent and more serious, and in particularly severe cases, are likely to lead to deterioration of other brain functions (inducting cognitive function) as well as physical impairments.
The current state of the art in treating neurological disorders, particularly epilepsy, typically involves drug therapy and surgery. The first approach is usually drug therapy.
A number of drugs are approved and available for treating epilepsy, such as sodium valproate, phenobarbital/primidone, ethosuximide, gabapentin, phenytoin, and carbamazepine, as well as a number of others. Unfortunately, those drugs typically have serious side effects, especially toxicity, and it is extremely important in most cases to maintain a precise therapeutic serum level to avoid breakthrough seizures (if the dosage is too low) or toxic effects (if the dosage is too high). The need for patient discipline is high, especially when a patient's drug regimen causes unpleasant side effects the patient may wish to avoid.
Moreover, while many patients respond well to drug therapy alone, a significant number (at least 20-30%) do not. For those patients, surgery is presently the best-established and most viable alternative course of treatment.
Currently practiced surgical approaches include radical surgical resection such as hemispherectomy, corticectomy, lobectomy and partial lobectomy, and less-radical lesionectomy, transection, and stereotactic ablation. Besides being less than fully successful, these surgical approaches generally have a high risk of complications, and can often result in damage to eloquent (i.e., functionally important) brain regions and the consequent long-term impairment of various cognitive and other neurological functions. Furthermore, for a variety of reasons, such surgical treatments are contraindicated in a substantial number of patients. And unfortunately, even after radical brain surgery, many epilepsy patients are still not seizure-free.
Electrical stimulation is an emerging therapy for treating epilepsy. However, currently approved and available electrical stimulation devices apply continuous electrical stimulation to neural tissue surrounding or near implanted electrodes, and do not perform any detection—they are not responsive to relevant neurological conditions.
The NeuroCybernetic Prosthesis (NCP) from Cyberonics, for example, applies continuous electrical stimulation to the patient's vagus nerve. This approach has been found to reduce seizures by about 50% in about 50% of patients. Unfortunately, a much greater reduction in the incidence of seizures is needed to provide substantial clinical benefit.
The Activa device from Medtronic is a pectorally implanted continuous deep brain stimulator intended primarily to treat Parkinson's disease. In operation, it supplies a continuous electrical pulse stream to a selected deep brain structure where an electrode has been implanted. Continuous stimulation of deep brain structures for the treatment of epilepsy has not met with consistent success. To be effective in terminating seizures, it is believed that one effective site where stimulation should be performed is near the focus of the epileptogenic region. The focus is often in the neocortex, where continuous stimulation may cause significant neurological deficit with clinical symptoms including loss of speech, sensory disorders, or involuntary motion. Accordingly, research has been directed toward automatic responsive epilepsy treatment based on a detection of imminent seizure.
A typical epilepsy patient experiences episodic attacks or seizures, which are generally electrographically defined as periods of abnormal neurological activity. As is traditional in the art, such periods shall be referred to herein as “ictal”.
Most prior work on the detection and responsive treatment of seizures via electrical stimulation has focused on analysis of electroencephalogram (BEG) and electrocorticogram (ECoG) waveforms. In common usage, the term “EEG” is often used to refer to signals representing aggregate neuronal activity potentials detectable via electrodes applied to a patient's scalp, though the term can also refer to signals obtained from deep in the patient's brain via depth electrodes and the like. Specifically, “ECoGs” refer to signals obtained from internal electrodes near the surface of the brain (generally on or under the dura mater); an ECoG is a particular type of EEG. Unless the context clearly and expressly indicates otherwise, the term “EEG” shall be used generically herein to refer to both EEG and ECoG signals, regardless of where in the patient's brain the electrodes are located.
Much of the work on detection has focused on the use of time-domain analysis of EEG signals. See, e.g., J. Gotman, Automatic seizure detection: improvements and evaluation, Electroencephalogr. Clin. Neurophysiol. 1990; 76(4) 317-24. In a typical time-domain detection system, EEG signals are received by one or more implanted electrodes and then processed by a control module, which then is capable of performing an action (intervention, warning, recording, etc.) when an abnormal neurological event is detected.
It is generally preferable to be able to detect and treat a seizure at or near its beginning, or even before it begins. The beginning of a seizure is referred to herein as an “onset.” However, it is important to note that there are two general varieties of seizure onsets. A “clinical onset” represents the beginning of a seizure as manifested through observable clinical symptoms, such as involuntary muscle movements or neurophysiological effects such as lack of responsiveness. An “electrographic onset” refers to the beginning of detectable electrographic activity indicative of a seizure. An electrographic onset will frequently occur before the corresponding clinical onset, enabling intervention before the patient suffers symptoms, but that is not always the case. In addition, there are changes in the EEG that occur seconds or even minutes before the electrographic onset that can be identified and used to facilitate intervention before electrographic or clinical onsets occur. This capability would be considered seizure prediction, in contrast to the detection of a seizure or its onset.
In the Gotman system, EEG waveforms are filtered and decomposed into “features” representing characteristics of interest in the waveforms. One such feature is characterized by the regular occurrence (i.e., density) of half-waves exceeding a threshold amplitude occurring in a specified frequency band between approximately 3 Hz and 20 Hz, especially in comparison to background (non-ictal) activity. When such half-waves are detected, it is believed that seizure activity is occurring For related approaches, see also H. Qu and J. Gotman, A seizure warning system for long term epilepsy monitoring, Neurology 1995; 45: 2250-4; and H. Qu and J. Gotman, A Patient-Specific Algorithm for the Detection of Seizure Onset in Long-Term EEG Monitoring: Possible Use as a Warning Device, IEEE Trans. Biomed. Eng. 1997; 44(2): 115-22.
The Gotman articles address half wave characteristics in general, and introduce a variety of measurement criteria, including a ratio of current epoch amplitude to background; average current epoch EEG frequency; average background EEG frequency; coefficient of variation of wave duration; ratio of current epoch amplitude to the amplitude of the following time period; average wave amplitude; average wave duration; dominant frequency (peak frequency of the dominant peak); and average power in a main energy zone. These criteria are variously mapped into an n-dimensional space, and whether a seizure is detected depends on the vector distance between the parameters of a measured segment of EEG and a seizure template in that space.
It should be noted that the schemes set forth in the above articles are not tailored for use in an implantable device, and hence typically require more computational ability than would be available in such a device.
U.S. Pat. No. 6,018,682 to Rise describes an implantable seizure warning system that implements a form of the Gotman system. However, the system described therein uses only a single detection modality, namely a count of sharp spike and wave patterns within a time period. This is accomplished with relatively complex processing, including averaging over time and quantifying sharpness by way of a second derivative of the signal. The Rise patent does not disclose how the signals are processed at a low level, nor does it explain detection criteria in any specific level of detail.
A more computationally demanding approach is to transform EEG signals into the frequency domain for rigorous spectrum analysis. See, e.g., U.S. Pat. No. 5,995,868 to Dorfmeister et al., which analyzes the power spectral density of EEG signals in comparison to background characteristics. Although this approach is generally believed to achieve good results, for the most part, its computational expense renders it less than optimal for use in long-term implanted epilepsy monitor and treatment devices. With current technology, the battery life in an implantable device computationally capable of performing the Dorfmeister method would be too short for it to be feasible.
Also representing an alternative and more complex approach is U.S. Pat. No. 5,857,978 to Hively et al., in which various non-linear and statistical characteristics of EEG signals are analyzed to identify the onset of ictal activity. Once more, the calculation of statistically relevant characteristics is not believed to be feasible in an implantable device.
U.S. Pat. No. 6,016,449 to Fischell, et al. (which is hereby incorporated by reference as though set forth in full herein), describes an implantable seizure detection and treatment system. In the Fischell system, various detection methods are possible, all of which essentially rely upon the analysis (either in the time domain or the frequency domain) of processed EEG signals. Fischell's controller is preferably implanted intracranially, but other approaches are also possible, including the use of an external controller. The processing and detection techniques applied in Fischell are generally well suited for implantable use. When a seizure is detected, the Fischell system applies responsive electrical stimulation to terminate the seizure, a capability that will be discussed in further detail below.
All of these approaches provide useful information, and in some cases may provide sufficient information for accurate detection and prediction of most imminent epileptic seizures.
However, the known approaches generally observe electrographic patterns at a single moment in time (or over a region of time reduced to one or more features); they do not differentiate between different patterns or sequences of electrographic activity, separated in time or space. It is believed that spatiotemporal patterns and sequences may serve to distinguish activity of interest from background activity, where looking at small portions of signals in isolation might not.
Neurological events are often characterized by evolving patterns and sequences of activity. For example, seizure onsets are often defined by different morphological patterns. See, e.g., Spencer S S, Guimaraes P, Katz A, Kim J, Spencer D. Morphological patterns of seizures recorded intracranially. Epilepsia 1992; 33: 537-545; and Lee S A, Spencer D D, Spencer S S. Intracranial EEG seizure-onset patterns in neocortical epilepsy. Epilepsia 2000; 41: 297-307.
Even within an onset or seizure, the most prominent characteristic may be changes in a signal rather than the signal itself at a snapshot in time. Schiff S J, Colella D, Jacyna G M et al. Brain chirps: spectrographic signatures of epileptic seizures. Clin. Neurophysiol. 2000; 111: 953-958; and Bergey G K, Franaszczuk P J. Epileptic seizures are characterized by changing signal complexity. Clin. Neurophysiol. 2001; 112: 241-249.
Spatial patterns (e.g., where an onset occurs) may be related to temporal patterns and signal morphology, and vice versa. It is believed that the most successful treatment strategies may be dependent upon seizure origin and type. Velasco A L, Wilson C L, Babb T L, Engel J Jr. Functional and anatomic correlates of two frequently observed temporal lobe seizure-onset patterns. Neural Plast. 2000; 7: 49-63. Thus, it would be desirable to be able to identify different patterns and morphologies to provide the best possible therapy to the patient.
Spatial patterns, represented by activity from different parts of the brain at the same time or at different times, may also come into play. For example, if it can be determined that seizure activity has propagated from one location to another, it may be advantageous to apply a therapy that takes that into account (rather than performing focal stimulation wherever the activity is first seen). See, e.g., Schiller Y, Cascino G D, Busacker N E, Sharbrough F W. Characterization and comparison of local onset and remote propagated electrographic seizures recorded with intracranial electrodes. Epilepsia 1998; 39: 380-388. An indication that a seizure has originated in a certain part of the brain may lead to improved treatment possibilities.
It has also been shown that electrographic signatures start to evolve long before a seizure begins. Litt B, Esteller R, Echauz J et al. Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron 2001; 30: 51-64. Accordingly, seizure prediction may be accomplished by identifying this slow evolution of signal characteristics over time, leading to the possibility of effective preventative therapy.
Present state-of-the-art seizure and neurological event detection technologies are generally based upon a single combination of characteristics—representative of a current neurological point in time or derived from a feature that tends to smear together signal characteristics over a time period.
Accordingly, and for the reasons set forth above, it would be advantageous to have the benefit of a neurological event detector that is capable of resolving time sequences and correlating spatial patterns. Such a detector would be capable of identifying temporal, spatial, and spatiotemporal patterns and sequences of neurological activity. As described above, this would tend to facilitate improved detection performance, improved therapy, and the possibility of accurate seizure prediction.
Two types of detection errors are generally possible. A “false positive,” as the term is used herein, refers to a detection of a seizure or ictal activity when no seizure or other abnormal event is actually occurring. Similarly, a “false negative” herein refers to the failure to detect a seizure or ictal activity that actually is occurring or shortly will occur.
In most cases, with all known implementations of the known approaches to detecting abnormal seizure activity solely by monitoring and analyzing individual segments of EEG activity, when a seizure detection algorithm is tuned to catch all seizures, there will be a significant number of false positives. While it is currently believed that there are minimal or no side effects to limited amounts of over-stimulation (e.g., providing stimulation sufficient to terminate a seizure in response to a false positive), the possibility of accidentally initiating a seizure or increasing the patient's susceptibility to seizures must be considered.
As is well known, it has been suggested that it is possible to treat and terminate seizures by applying electrical stimulation to the brain. See, e.g., U.S. Pat. No. 6,016,449 to Fischell et al., and H. R. Wagner, et al., Suppression of cortical epileptiform activity by generalized and localized ECoG desynchronization, Electroencephalogr. Clin. Neurophysiol. 1975; 39(5): 499-506. And as stated above, it is believed to be beneficial to perform this stimulation only when a seizure (or other undesired neurological event) is occurring or about to occur, as inappropriate stimulation may result in the initiation of seizures.
Furthermore, it should be noted that a false negative (that is, a seizure that occurs without any warning or treatment from the device) will often cause the patient significant discomfort and detriment. Clearly, false negatives are to be avoided.
It has been found to be difficult to achieve an acceptably low level of false positives and false negatives with the level of computational ability available in an implantable device with reasonable battery life.
Preferably, the battery in an implantable device, particularly one implanted intracranially, should last at least several years. There is a substantial risk of complications (such as infection, blood clots, and the overgrowth of scar tissue) and lead failure each time an implanted device or its battery is replaced. Rechargeable batteries have not been found to provide any advantage in this regard, as they are not as efficient as traditional cells, and the additional electronic circuitry required to support the recharging operation contributes to the device's size and complexity. Moreover, there is a need for patient discipline in recharging the device batteries, which calls for the frequent transmission of a substantial amount of power over a wireless link and through the patient's skin and other tissue.
As stated above, the detection and prediction of ictal activity has traditionally required a significant amount of computational ability. Moreover, for an implanted device to have significant real-world utility, it is also advantageous to include a number of other features and capabilities. Specifically, treatment (via electrical stimulation or drug infusion) and/or warning (via an audio annunciator, for example), recording of EEG signals for later consideration and analysis, and telemetry providing a link to external equipment are all useful capabilities for an implanted device capable of detecting or predicting epileptiform signals. All of these additional subsystems will consume further power.
Moreover, size is also a consideration. For various reasons, intracranial implants are favored. A device implanted intracranially (or under the scalp) will typically have a lower risk of failure than a similar device implanted pectorally or elsewhere, which require a lead to be run from the device, through the patient's neck to the electrode implantation sites in the patient's head. This lead is also prone to receive additional electromagnetic interference.
As is well known in the art, the computational ability of a processor-controlled system is directly related to both size and power consumption. In accordance with the above considerations, therefore, it would be advantageous to have sufficient detection and prediction capabilities to avoid a substantial number of false positive and false negative detections, and yet consume little enough power (in conjunction with the other subsystems) to enable long battery life. Such an implantable device would have a relatively low-power central processing unit to reduce the electrical power consumed by that portion.
At the current time, there is no known implantable device that is capable of detecting and predicting seizures based on spatiotemporal patterns and sequences.